Kanhao Xue

Abstract Memristive in-memory computing has demonstrated potential for solving matrix equations in scientific computing. However, the inherent inaccuracies of analog mechanisms create challenges in achieving high-precision solutions while maintaining low-energy consumption. This study introduces a memristive matrix equation solver that considerably accelerates solutions by performing mathematical iterations directly within an analog domain.

Abstract Nonlinear optical responses in two-dimensional (2D) materials can build free-space optical neuromorphic computing systems. Ensuring the high performance and the tunability of the system is essential to encode diverse functions. However, common strategies, including the integration of external electrode arrays or photonic structures with 2D materials, and barely patterned 2D materials, exhibit a contradiction between performance and tunability.